Routing Attacks Detection Method of Wireless Sensor Network

Yongzhong Li, Miao Du, Yi Li

Abstract


Security is a critical challenge for creating robust and reliable sensor networks. For the particularity and security threats of wireless sensor networks, we proposed an anomaly detection method based on particle swarm optimization K-means clustering algorithm to detect routing attacks caused by abnormal flows in this paper. K-means clustering algorithm is an effective method has been proved for apply to the intrusion detection system, but it is a part of the optimal solution, rather than the overall optimal solution. Particle swarm optimization (PSO) algorithm which is evolutionary computation technology based on swarm intelligence has good global search ability. With the deficiency of global search ability for K-means clustering algorithm, K-means clustering algorithm based on particle swarm optimization (PSO-KM) is proposed in this paper. This algorithm could overcome falling into local minima and has relatively good overall convergence. Experiments on data sets KDD CUP 99 validate the effectiveness of the proposed method and show that the method has higher detection rate and lower false detection rate.

Keywords


Wirelesssensor networks; PSO; K-means clustering; Intrusion detection system


DOI
10.12783/dtcse/wicom2018/26273

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